text: import abc
  from collections import defaultdict
  from datetime import datetime
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  
  import pandas
  import pyarrow
  from tqdm import tqdm
  
  from feast import errors, importer
  from feast.entity import Entity
  from feast.feature_table import FeatureTable
  from feast.feature_view import DUMMY_ENTITY_ID, FeatureView
  from feast.infra.offline_stores.offline_store import RetrievalJob
  from feast.on_demand_feature_view import OnDemandFeatureView
  from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
  from feast.protos.feast.types.Value_pb2 import Value as ValueProto
  from feast.registry import Registry
  from feast.repo_config import RepoConfig
  from feast.type_map import python_value_to_proto_value
  
  
  class Provider(abc.ABC):
      @abc.abstractmethod
      def __init__(self, config: RepoConfig):
          ...
  
      @abc.abstractmethod
      def update_infra(
          self,
          project: str,
          tables_to_delete: Sequence[Union[FeatureTable, FeatureView]],
          tables_to_keep: Sequence[Union[FeatureTable, FeatureView]],
          entities_to_delete: Sequence[Entity],
          entities_to_keep: Sequence[Entity],
          partial: bool,
      ):
          """
          Reconcile cloud resources with the objects declared in the feature repo.
  
          Args:
              project: Project to which tables belong
              tables_to_delete: Tables that were deleted from the feature repo, so provider needs to
                  clean up the corresponding cloud resources.
              tables_to_keep: Tables that are still in the feature repo. Depending on implementation,
                  provider may or may not need to update the corresponding resources.
              entities_to_delete: Entities that were deleted from the feature repo, so provider needs to
                  clean up the corresponding cloud resources.
              entities_to_keep: Entities that are still in the feature repo. Depending on implementation,
                  provider may or may not need to update the corresponding resources.
              partial: if true, then tables_to_delete and tables_to_keep are *not* exhaustive lists.
                  There may be other tables that are not touched by this update.
          """
          ...
  
      @abc.abstractmethod
      def teardown_infra(
          self,
          project: str,
          tables: Sequence[Union[FeatureTable, FeatureView]],
          entities: Sequence[Entity],
      ):
          """
          Tear down all cloud resources for a repo.
  
          Args:
              project: Feast project to which tables belong
              tables: Tables that are declared in the feature repo.
              entities: Entities that are declared in the feature repo.
          """
          ...
  
      @abc.abstractmethod
      def online_write_batch(
          self,
          config: RepoConfig,
          table: Union[FeatureTable, FeatureView],
          data: List[
              Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
          ],
          progress: Optional[Callable[[int], Any]],
      ) -> None:
          """
          Write a batch of feature rows to the online store. This is a low level interface, not
          expected to be used by the users directly.
  
          If a tz-naive timestamp is passed to this method, it is assumed to be UTC.
  
          Args:
              config: The RepoConfig for the current FeatureStore.
              table: Feast FeatureTable
              data: a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,
                  a dict containing feature values, an event timestamp for the row, and
                  the created timestamp for the row if it exists.
              progress: Optional function to be called once every mini-batch of rows is written to
                  the online store. Can be used to display progress.
          """
          ...
  
      @abc.abstractmethod
      def materialize_single_feature_view(
          self,
          config: RepoConfig,
          feature_view: FeatureView,
          start_date: datetime,
          end_date: datetime,
          registry: Registry,
          project: str,
          tqdm_builder: Callable[[int], tqdm],
      ) -> None:
          pass
  
      @abc.abstractmethod
      def get_historical_features(
          self,
          config: RepoConfig,
          feature_views: List[FeatureView],
          feature_refs: List[str],
          entity_df: Union[pandas.DataFrame, str],
          registry: Registry,
          project: str,
          full_feature_names: bool,
      ) -> RetrievalJob:
          pass
  
      @abc.abstractmethod
      def online_read(
          self,
          config: RepoConfig,
          table: Union[FeatureTable, FeatureView],
          entity_keys: List[EntityKeyProto],
          requested_features: List[str] = None,
      ) -> List[Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]]:
          """
          Read feature values given an Entity Key. This is a low level interface, not
          expected to be used by the users directly.
  
          Returns:
              Data is returned as a list, one item per entity key. Each item in the list is a tuple
              of event_ts for the row, and the feature data as a dict from feature names to values.
              Values are returned as Value proto message.
          """
          ...
  
  
  def get_provider(config: RepoConfig, repo_path: Path) -> Provider:
      if "." not in config.provider:
          if config.provider in {"gcp", "aws", "local"}:
              if config.provider == "aws":
                  from feast.infra.aws import AwsProvider
  
                  return AwsProvider(config)
  
              from feast.infra.passthrough_provider import PassthroughProvider
  
              return PassthroughProvider(config)
          else:
              raise errors.FeastProviderNotImplementedError(config.provider)
      else:
          # Split provider into module and class names by finding the right-most dot.
          # For example, provider 'foo.bar.MyProvider' will be parsed into 'foo.bar' and 'MyProvider'
          module_name, class_name = config.provider.rsplit(".", 1)
  
          cls = importer.get_class_from_type(module_name, class_name, "Provider")
  
          return cls(config)
  
  
  def _get_requested_feature_views_to_features_dict(
      feature_refs: List[str],
      feature_views: List[FeatureView],
      on_demand_feature_views: List[OnDemandFeatureView],
  ) -> Tuple[Dict[FeatureView, List[str]], Dict[OnDemandFeatureView, List[str]]]:
      """Create a dict of FeatureView -> List[Feature] for all requested features.
      Set full_feature_names to True to have feature names prefixed by their feature view name."""
  
      feature_views_to_feature_map: Dict[FeatureView, List[str]] = defaultdict(list)
      on_demand_feature_views_to_feature_map: Dict[
          OnDemandFeatureView, List[str]
      ] = defaultdict(list)
  
      for ref in feature_refs:
          ref_parts = ref.split(":")
          feature_view_from_ref = ref_parts[0]
          feature_from_ref = ref_parts[1]
  
          found = False
          for feature_view_from_registry in feature_views:
              if feature_view_from_registry.name == feature_view_from_ref:
                  found = True
                  feature_views_to_feature_map[feature_view_from_registry].append(
                      feature_from_ref
                  )
          for odfv_from_registry in on_demand_feature_views:
              if odfv_from_registry.name == feature_view_from_ref:
                  found = True
                  on_demand_feature_views_to_feature_map[odfv_from_registry].append(
                      feature_from_ref
                  )
  
          if not found:
              raise ValueError(f"Could not find feature view from reference {ref}")
  
      return feature_views_to_feature_map, on_demand_feature_views_to_feature_map
  
  
  def _get_column_names(
      feature_view: FeatureView, entities: List[Entity]
  ) -> Tuple[List[str], List[str], str, Optional[str]]:
      """
      If a field mapping exists, run it in reverse on the join keys,
      feature names, event timestamp column, and created timestamp column
      to get the names of the relevant columns in the offline feature store table.
  
      Returns:
          Tuple containing the list of reverse-mapped join_keys,
          reverse-mapped feature names, reverse-mapped event timestamp column,
          and reverse-mapped created timestamp column that will be passed into
          the query to the offline store.
      """
      # if we have mapped fields, use the original field names in the call to the offline store
      event_timestamp_column = feature_view.batch_source.event_timestamp_column
      feature_names = [feature.name for feature in feature_view.features]
      created_timestamp_column = feature_view.batch_source.created_timestamp_column
      join_keys = [
          entity.join_key for entity in entities if entity.join_key != DUMMY_ENTITY_ID
      ]
      if feature_view.batch_source.field_mapping is not None:
          reverse_field_mapping = {
              v: k for k, v in feature_view.batch_source.field_mapping.items()
          }
          event_timestamp_column = (
              reverse_field_mapping[event_timestamp_column]
              if event_timestamp_column in reverse_field_mapping.keys()
              else event_timestamp_column
          )
          created_timestamp_column = (
              reverse_field_mapping[created_timestamp_column]
              if created_timestamp_column
              and created_timestamp_column in reverse_field_mapping.keys()
              else created_timestamp_column
          )
          join_keys = [
              reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col
              for col in join_keys
          ]
          feature_names = [
              reverse_field_mapping[col] if col in reverse_field_mapping.keys() else col
              for col in feature_names
          ]
  
      # We need to exclude join keys and timestamp columns from the list of features, after they are mapped to
      # their final column names via the `field_mapping` field of the source.
      _feature_names = set(feature_names) - set(join_keys)
      _feature_names = _feature_names - {event_timestamp_column, created_timestamp_column}
      feature_names = list(_feature_names)
      return (
          join_keys,
          feature_names,
          event_timestamp_column,
          created_timestamp_column,
      )
  
  
  def _run_field_mapping(
      table: pyarrow.Table, field_mapping: Dict[str, str],
  ) -> pyarrow.Table:
      # run field mapping in the forward direction
      cols = table.column_names
      mapped_cols = [
          field_mapping[col] if col in field_mapping.keys() else col for col in cols
      ]
      table = table.rename_columns(mapped_cols)
      return table
  
  
  def _convert_arrow_to_proto(
      table: pyarrow.Table, feature_view: FeatureView, join_keys: List[str],
  ) -> List[Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]]:
      rows_to_write = []
  
      def _coerce_datetime(ts):
          """
          Depending on underlying time resolution, arrow to_pydict() sometimes returns pandas
          timestamp type (for nanosecond resolution), and sometimes you get standard python datetime
          (for microsecond resolution).
  
          While pandas timestamp class is a subclass of python datetime, it doesn't always behave the
          same way. We convert it to normal datetime so that consumers downstream don't have to deal
          with these quirks.
          """
  
          if isinstance(ts, pandas.Timestamp):
              return ts.to_pydatetime()
          else:
              return ts
  
      for row in zip(*table.to_pydict().values()):
          entity_key = EntityKeyProto()
          for join_key in join_keys:
              entity_key.join_keys.append(join_key)
              idx = table.column_names.index(join_key)
              value = python_value_to_proto_value(row[idx])
              entity_key.entity_values.append(value)
          feature_dict = {}
          for feature in feature_view.features:
              idx = table.column_names.index(feature.name)
              value = python_value_to_proto_value(row[idx], feature.dtype)
              feature_dict[feature.name] = value
          event_timestamp_idx = table.column_names.index(
              feature_view.batch_source.event_timestamp_column
          )
          event_timestamp = _coerce_datetime(row[event_timestamp_idx])
  
          if feature_view.batch_source.created_timestamp_column:
              created_timestamp_idx = table.column_names.index(
                  feature_view.batch_source.created_timestamp_column
              )
              created_timestamp = _coerce_datetime(row[created_timestamp_idx])
          else:
              created_timestamp = None
  
          rows_to_write.append(
              (entity_key, feature_dict, event_timestamp, created_timestamp)
          )
      return rows_to_write
  